Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Vital-sign monitoring and spatial tracking of multiple people using a contactless radar-based sensor

Nature Electronics volume 2, pages 252–262 (2019)Cite this article

Subjects

Abstract

Various medical systems exist for monitoring people in daily life, but they typically require the patient to wear a device, which can create discomfort and can limit long-term use. Contactless vital-sign monitoring would be preferable, but such technology is challenging to develop as it involves weak signals that need to be accurately detected within a practical distance, while being reliably distinguished from unwanted disturbance. Here, we show that a radar-based sensor can be used to monitor the individual vital signs (heartbeat and respiration) of multiple people in a real-world setting. The contactless approach, which does not require any body parts to be worn, uses two antennas (one transmitter and one receiver) and algorithms for target tracking and rejection of random body movements. As a result, it is robust against moderate random body movements (limb movements and desk work) and can keep track of individual people during vigorous movement (such as walking and standing up).

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Experimental set-up.
Fig. 2: Range and Doppler prolife methodology.
Fig. 3: People-tracking experimental results.
Fig. 4: Comparison of linear demodulation with classic phase extraction for vital signs detection.
Fig. 5: Vital-sign monitoring experimental result.
Fig. 6: Random body movement rejection experimental result.

Similar content being viewed by others

Data availability

The data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request.

References

  1. Scully, T. Demography: to the limit. Nature 492, 2–3 (2012).

    Article  Google Scholar 

  2. Fontana, L., Kennedy, B. K., Longo, V. D., Seals, D. & Melov, S. Medical research: treat ageing. Nature 511, 405–407 (2014).

    Article  Google Scholar 

  3. Hung, W. W., Ross, J. S., Boockvar, K. S. & Siu, A. L. Recent trends in chronic disease, impairment and disability among older adults in the United States. BMC Geriatr. 11, 47 (2011).

    Article  Google Scholar 

  4. Gulley, S., Rasch, E. & Chan, L. If we build it, who will come? Working-age adults with chronic health care needs and the medical home. Med. Care 49, 149–155 (2011).

    Article  Google Scholar 

  5. Abdelhafiz, A. H. Heart failure in older people: causes, diagnosis and treatment. Age Ageing 31, 29–36 (2002).

    Article  Google Scholar 

  6. Global diffusion of eHealth: Making universal health coverage achievable. Report of the third global survey on eHealth (World Health Organization, 2016).

  7. Soh, P. J., Vandenbosch, G. A. E., Mercuri, M. & Schreurs, D. Wearable wireless health monitoring: current developments, challenges and future trends. IEEE Microw. Mag. 55, 55–70 (2015).

    Article  Google Scholar 

  8. Hao, Y. & Foster, R. Wireless body sensor networks for health monitoring applications. Physiol. Meas. 29, 27–57 (2008).

    Article  Google Scholar 

  9. Patel, S., Park, H., Bonato, P., Chan, L. & Rodgers, M. A review of wearable sensors and systems with application in rehabilitation. J. Neuroeng. Rehabil. 9, 21 (2012).

    Article  Google Scholar 

  10. Son, D. et al. Multifunctional wearable devices for diagnosis and therapy of movement disorders. Nat. Nanotechnol. 9, 397–404 (2014).

    Article  Google Scholar 

  11. Gualandi, I. et al. Textile organic electrochemical transistors as a platform for wearable biosensors. Sci. Rep. 6, 33637 (2016).

    Article  Google Scholar 

  12. Imani, S. et al. A wearable chemical-electrophysiological hybrid biosensing system for real-time health and fitness monitoring. Nat. Commun. 7, 11650 (2016).

    Article  Google Scholar 

  13. Jung, S. et al. Wearable fall detector using integrated sensors and energy devices. Sci. Rep. 5, 17081 (2015).

    Article  Google Scholar 

  14. Lobodzinski, S. S. & Laks, M. M. New devices for very long-term ECG monitoring. Cardiol. J. 19, 210–214 (2012).

    Article  Google Scholar 

  15. Shafiq, G. & Veluvolu, K. C. Surface chest motion decomposition for cardiovascular monitoring. Sci. Rep. 4, 5093 (2014).

    Article  Google Scholar 

  16. Pandian, P. S. et al. Smart vest: wearable multi-parameter remote physiological monitoring system. Med. Eng. Phys. 30, 466–477 (2008).

    Article  Google Scholar 

  17. Bourke, A. K., O'Brien, J. V. & Lynos, G. M. Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait Posture 26, 194–199 (2007).

    Article  Google Scholar 

  18. Amelard, R. et al. Feasibility of long-distance heart rate monitoring using transmittance photoplethysmographic imaging (PPGI). Sci. Rep. 5, 14637 (2015).

    Article  Google Scholar 

  19. Nomura, K. et al. A flexible proximity sensor formed by duplex screen/screen-offset printing and its application to non-contact detection of human breathing. Sci. Rep. 6, 19947 (2016).

    Article  Google Scholar 

  20. Coronato, A. Uranus: a middleware architecture for dependable AAL and vital signs monitoring applications. Sensors 12, 3145–3161 (2012).

    Article  Google Scholar 

  21. Partridge, L. Gerontology: extending the healthspan. Nature 529, 154 (2016).

    Article  Google Scholar 

  22. National Research Council (US) Panel on a Research Agenda and New Data for an Aging World Preparing for an Aging World: The Case for Cross-National Research (US National Academies Press, 2001).

  23. eHealth WHA58.28 (World Health Organization, 2005); https://www.who.int/healthacademy/media/WHA58-28-en.pdf

  24. Korhonen, I. et al. Health monitoring in the home of the future. IEEE Eng. Med. Biol. Mag. 22, 66–73 (2003).

    Article  Google Scholar 

  25. Levine, C. Home sweet hospital: the nature and limits of private responsibilities for home health care. J. Aging Health 11, 341–359 (1999).

    Article  Google Scholar 

  26. Li, C. et al. Radar remote monitoring of vital signs. IEEE Microw. Mag. 10, 47–56 (2009).

    Article  Google Scholar 

  27. Obeid, D., Zaharia, G., Sadek, S. & Zein, G. Microwave Doppler radar for heartbeat detection vs electrocardiogram. Microw. Opt. Technol. Lett. 54, 2610–2617 (2012).

    Article  Google Scholar 

  28. Li, C. et al. A review on recent progress of portable short-range noncontact microwave radar systems. IEEE Trans. Microw. Theory Tech. 65, 1692–1706 (2017).

    Article  Google Scholar 

  29. Mercuri, M. et al. Analysis of an indoor biomedical radar-based system for health monitoring. IEEE Trans. Microw. Theory Tech. 61, 2061–2068 (2013).

    Article  Google Scholar 

  30. Su, B., Ho, K., Rantz, M. & Skubic, M. Doppler radar fall activity detection using the wavelet transform. IEEE Trans. Biomed. Eng. 62, 865–875 (2015).

    Article  Google Scholar 

  31. Wang, F. K., Chou, Y. R., Chiu, Y. C. & Horng, T. S. Chest-worn health monitor based on a bistatic self-injection-locked radar. IEEE Trans. Biomed. Eng. 62, 2931–2940 (2015).

    Article  Google Scholar 

  32. Morgan, D. R. & Zierdt, M. G. Novel signal processing techniques for Doppler radar cardiopulmonary sensing. Signal Process. 89, 45–66 (2009).

    Article  Google Scholar 

  33. Mercuri, M. et al. Frequency-tracking CW Doppler radar solving small-angle approximation and null point issues in non-contact vital signs monitoring. IEEE Trans. Biomed. Circuits Syst. 11, 671–680 (2017).

    Article  Google Scholar 

  34. Mercuri, M. et al. A direct phase-tracking doppler radar using wavelet independent component analysis for non-contact respiratory and heart rate monitoring. IEEE Trans. Biomed. Circuits Syst. 12, 632–643 (2018).

    Article  Google Scholar 

  35. Mikhelson, I. V. et al. Remote sensing of patterns of cardiac activity on an ambulatory subject using millimeter-wave interferometry and statistical methods. Med. Biol. Eng. Comput. 51, 135–142 (2013).

    Article  Google Scholar 

  36. Li, C. & Jenshan, L. Random body movement cancellation in Doppler radar vital sign detection. IEEE Trans. Microw. Theory Tech. 56, 3143–3152 (2008).

    Article  Google Scholar 

  37. Peng, Z. et al. A portable FMCW interferometry radar with programmable low-IF architecture for localization, ISAR imaging and vital sign tracking. IEEE Trans. Microw. Theory Tech. 65, 1334–1344 (2017).

    Article  Google Scholar 

  38. Hui, X. & Kan, E. C. Monitoring vital signs over multiplexed radio by near-field coherent sensing. Nat. Electron. 1, 74–78 (2018).

    Article  Google Scholar 

  39. Adib, F., Kabelac, Z., Katabi, D. & Miller, R. C. 3D Tracking via body radio reflections. Proc. Usenix NSDI'14 (2014); http://witrack.csail.mit.edu/witrack-paper.pdf

  40. Adib, F., Kabelac, Z. & Katabi, D. Multi-person localization via RF body reflections. Proc. Usenix NSDI'15 (2015); http://witrack.csail.mit.edu/witrack2-paper.pdf

  41. Adib, F., Mao, H., Kabelac, Z., Katabi, D. & Miller R. C. Smart homes that monitor breathing and heart rate. Proc. ACM CHI https://doi.org/10.1145/2702123.2702200 (2015).

  42. Su, B., Ho, K., Rantz, M. & Skubic, M. Doppler radar fall activity detection using the wavelet transform. IEEE Trans. Biomed. Circuits Syst. 62, 865–875 (2015).

    Google Scholar 

  43. Wang, F., Skubic, M., Rantz, M. & Cuddihy, P. E. Quantitative gait measurement with pulse-Doppler radar for passive in-home gait assessment. IEEE Trans. Biomed. Circuits Syst. 61, 2434–2443 (2014).

    Google Scholar 

  44. Taylor, J. D. Ultra-wideband Radar Technology (CRC Press, 2001).

  45. http://www.gtec.at/Products/Hardware-and-Accessories/g.USBamp-Specs-Features

Download references

Acknowledgements

The authors thank all participating volunteers, and E. Hermeling, E. Wentink and B. Grundlehner for their consistent and prompt evaluation regarding the safety and ethical aspects of our experimental protocols.

Author information

Authors and Affiliations

  1. IMEC – Netherlands, Eindhoven, The Netherlands

    Marco Mercuri, Ilde Rosa Lorato, Yao-Hong Liu, Fokko Wieringa & Chris Van Hoof

  2. Maastricht University, Maastricht, The Netherlands

    Fokko Wieringa

  3. IMEC, Leuven, Belgium

    Chris Van Hoof & Tom Torfs

  4. KU Leuven, Leuven, Belgium

    Chris Van Hoof

Authors
  1. Marco Mercuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ilde Rosa Lorato
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yao-Hong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fokko Wieringa
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chris Van Hoof
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tom Torfs
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.M. conceived and designed the systems and experiments, developed the range/Doppler profile methodology, analysed and interpreted the data, and wrote the paper. I.L. developed the range/Doppler profile and random body movement methodologies and processed, analysed, interpreted and plotted the data. Y.-H.L. provided technical expertise for the PLL implementation, measured the PLL phase noise and edited the manuscript. F.W. helped with designing the volunteer protocol for ethical approval, provided feedback on targeted medical applications as well as measurement validation and edited the manuscript. C.V.H. provided technical feedback, edited the manuscript and supervised the research. T.T. provided technical feedback, provided final editing of the manuscript and supervised the research.

Corresponding author

Correspondence to Marco Mercuri.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary Information

Supplementary Figs. 1–3

Reporting Summary

Supplementary Video

The video shows the radar Doppler signal and the extracted heartbeat and respiration signals of a sitting subject at 2.6 m who performed four different random body movements: moving an arm (at about 18 s), crossing the legs (at about 55 s), moving the torso back and forth (at about 75 s), and moving an arm (at about 105 s). The extracted biomedical signals have been compared with gold standard references (PPG and respiration belt).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mercuri, M., Lorato, I.R., Liu, YH. et al. Vital-sign monitoring and spatial tracking of multiple people using a contactless radar-based sensor. Nat Electron 2, 252–262 (2019). https://doi.org/10.1038/s41928-019-0258-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41928-019-0258-6

This article is cited by

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics